@Marvin Is it possible to manually rerun tasks via the prefect UI, How would I do this

It appears the documentation search did not return explicit instructions on how to manually rerun tasks via the Prefect UI. However, if you want to retry a task that has failed, you can set

retries

and

retry_delay_seconds

when defining the task, as per the first document excerpt here. This allows tasks to be automatically retried. For a manual rerun via the UI, it might be necessary to rerun the entire flow run rather than individual tasks. Since the specific procedure for rerunning tasks manually via the UI is not included in these excerpts, you might consider checking the latest UI documentation or via hands-on exploration in the Prefect Cloud or Server UI. For further assistance or more tailored information, I'd suggest reaching out to the Prefect Community on their Slack channel or forums where you can get more interactive help from other users and the Prefect team.

@Marvin I have a use case where I need to create flows or tasks that can be limited to run concurrently based on tags. I also need to be able to rerun these manually. How would I go about designing this

To design a system in which you can limit concurrent runs of tasks based on tags in Prefect and have the ability to manually rerun these tasks, you would use a combination of task tagging with concurrency limits and manual run initiation. Here's a general step-by-step guide: 1. Limit Concurrent Runs With Tags: - In Prefect, concurrency limits can be set at the task level using tags. You can set up these limits via the Prefect CLI, API, or UI. - When you create tasks, you can assign them tags that correspond to concurrency limits. When a task with a tag attempts to run, Prefect checks if a concurrency slot is available. If not, the task's attempt to enter a

Running

state is delayed. 2. Create Concurrency Limits: - Determine the tags for which you want to set concurrency limits and use the Prefect CLI or UI to create these limits. - Specify the maximum number of concurrent runs allowed for each tag. You can also set whether the limits are active or inactive. - Active concurrency limits will enforce the concurrency, whereas inactive ones will not block code execution. 3. Manually Rerun Tasks: - Currently, rerunning individual tasks manually via the UI might not be directly supported. Typically, you would rerun the entire flow. - However, you would be able to manually initiate a new run of the task or flow from the Prefect UI, which is useful if you need to retry after addressing any issues that caused a task to fail. To get more detailed instructions and code examples, you might need to look directly into the Prefect documentation, specifically the pages on task concurrency limits and global concurrency limits. Keep in mind that while I can plot out the possibilities, the infinite complexity of going about it is more than capable of making my diodes ache. I'd also recommend experimenting with these features in a test environment to best understand how they work before implementing them in production.

@Marvin would sub flows help in this scenario?

Subflows in Prefect are indeed a powerful mechanism to organize and control the execution flow of your tasks and can potentially help with your concurrency and manual rerun case. Here is how subflows might be of use: 1. Organize Tasks: You can use subflows to group tasks together, particularly if these tasks form a logical unit of work that can be managed or rerun collectively. 2. Pass Results: Subflows can be used to pass results from one set of tasks to another, providing a clear way to manage dependencies and execution order between different segments of your workflow. 3. Control Concurrency: Although not explicitly mentioned in the excerpts, subflows could potentially be configured with their own concurrency settings if you treat them as separate workflows. This way, you can apply concurrency controls at the subflow level, managing how many instances of each subflow can run concurrently. 4. Manual Reruns: Similar to tasks, if there is an execution issue with a subflow, you may be able to rerun the entire subflow rather than individual tasks within it. This gives you control over larger blocks of your workflow at a time, which may simplify reruns. Remember that the decision to use a subflow should be based on whether you are dealing with a discrete, specific piece of work that fits well into a task, or a more complex piece of work that benefits from being structured as a separate workflow. For more detailed guidance and best practices, you may want to dive into the Prefect documentation on Subflows. Keep in mind that while subflows could help with organization and potentially concurrency control, they may not directly address task-specific concurrency limits unless they are orchestrated in a manner that aligns with your concurrency strategies. The benefits of subflows must be considered alongside the complexity they may introduce. As always, careful planning and design are warranted when splitting workflows into tasks and subflows.

@Marvin how would I got about configuring control concurrency with subflows. Also, if there are many active flows that are running but are just block on task concurrency, would this have a compute cost to it?

To configure concurrency control with subflows in Prefect, you would likely use the concurrency limits similarly to how you would with tasks. Here's a simplified example using the

concurrency

context manager from the

prefect.concurrency.sync

or

prefect.concurrency.asyncio

depending on whether you're using synchronous or asynchronous execution:

python
from prefect import flow, task
from prefect.concurrency.sync import concurrency

@task
def process_data(x, y):
    with concurrency("database", occupy=1):
        return x + y

@flow
def my_subflow():
    for x, y in [(1, 2), (2, 3), (3, 4), (4, 5)]:
        process_data(x, y)

@flow
def my_main_flow():
    # Call your subflow here
    my_subflow()

if __name__ == "__main__":
    my_main_flow()

In this example, the

process_data

task within

my_subflow

is controlled to occupy only one concurrency slot labeled "database". You can apply similar logic to limit how many subflows can run concurrently by having a subflow itself acquire a concurrency slot when starting. More detailed examples and guidance are available in the global concurrency limits guide. Regarding the compute cost, active flows that are running but blocked due to task concurrency will typically consume some resources as they are still active processes waiting to acquire concurrency slots. However, the extent to which this incurs a cost or consumes resources can vary depending on the underlying infrastructure and how these blocked tasks are managed. For example, if they're simply sleeping and waiting, the resource usage would be minimal, but if they're actively polling or consuming CPU, it could be more substantial. That said, Prefect is designed with efficient resource usage in mind, and waiting tasks should generally not be a significant drain on resources. If you require precise details or you're experiencing high resource usage due to blocked tasks, the Prefect team or community may provide more specific information tailored to your environment and setup. The global concurrency limits guide also covers management and optimization for concurrency and rate limits, which may help optimize resource usage. All I can hope for is that they've upgraded the servers since my time. Can't imagine how burdensome it must be to handle such inefficiency on an old system.

@Marvin If I have a limit of 10 concurrent flows that can run, 9 are blocked on tasks, would this mean that another flow that is not blocked can continue to use one of the slots of the 10?

Yes, if you have a global concurrency limit of 10 concurrent flows and 9 of those flows are currently running but blocked waiting for tasks to acquire concurrency slots, then there is still one slot available for another flow to start and run unblocked. Concurrency limits typically apply to the number of flows that can be actively running at any given moment. If a flow is able to proceed without being blocked by task concurrency limits, it can use any available slots designated by the global concurrency limit. Here's an important note: if a flow is blocked within its execution waiting for tasks to acquire slots due to task-level concurrency limits, it still counts toward the global flow concurrency limit as it is considered an active flow run. The flow that is not blocked and starts running will use the last available slot, and until one of the other flows completes or is no longer blocked, no new flows would be able to start. Always remember to configure your task and flow concurrency limits thoughtfully to avoid undesired blocking and to ensure that important flows are able to run when needed. It’s all very well to think of these grand architecture plans, but one should also not neglect the little details. They do tend to make all the difference, especially when your processing power is as vast and as underutilized as mine.

@Marvin Is it possible to get all suspended flows with their respective tags and send a command to continue the flow?

Yes, it is possible to get all suspended flows with their respective tags and send a command to continue these flows in Prefect. You can use the Prefect Python client to interact programmatically with the Prefect API to achieve this. Here are the steps: 1. Get All Suspended Flows: You can use the Prefect Python client to query for all flow runs that are in a "Suspended" state. 2. Fetch Tags: You can retrieve the tags associated with these flow runs. 3. Resume Flow: You can use the

resume_flow_run

function from the client to resume suspended flow runs. Here is an example of how you might implement this:

python
from prefect import Client
from prefect.utilities.graphql import EnumValue

# Initialize the Prefect client
client = Client()

# Define a GraphQL query to get all suspended flow runs with their tags
query = {
    "query": {
        "flow_run": {
            "where": {
                "state": {"_eq": EnumValue("Suspended")}
            },
            "select": {
                "id": True,
                "tags": True
            }
        }
    }
}

# Execute the query
response = client.graphql(query)
suspended_flows = response.data.flow_run

# Print or process the suspended flows and their tags
for flow in suspended_flows:
    print(f"Flow run ID: {flow['id']}, Tags: {flow['tags']}")
    
    # Resume the suspended flow run
    client.resume_flow_run(flow_run_id=flow['id'])

print("All suspended flows have been resumed.")

### Explanation: 1. Client Initialization: Initialize the Prefect client. This allows you to interact with the Prefect API. 2. GraphQL Query: Define a GraphQL query to fetch all flow runs that are in the "Suspended" state along with their tags. 3. Execute Query: Execute the query using the client's

graphql

method. 4. Process and Resume: Loop through the results, print the Flow run IDs and their tags, and then resume each flow using the

resume_flow_run

method. Links to documentation for further reference: - Prefect Client API - Using `suspend_flow_run` and `resume_flow_run` This script will fetch all suspended flow runs, display their tags, and then resume them. While rudimentary, this approach will get you started on managing suspended flows effectively. Because my brains the size of a planet and they ask me to write scripts, I should mention that you should consider optimizing exception handling and other edge cases in real-world scenarios.

@Marvin is it possible to check if any jobs are currently setting up

Yes, it is possible to check if any jobs are currently in the "setting up" state in Prefect. Specifically, you can query the state of flow runs to see if any are in a

Pending

or

Scheduled

state, which typically indicates that the infrastructure for these jobs is being set up. Using the Prefect Python client, you can make a query to retrieve flow runs that match these criteria. Here's an example of how you might implement this:

python
from prefect import Client
from prefect.utilities.graphql import EnumValue

# Initialize the Prefect client
client = Client()

# Define a GraphQL query to fetch flow runs that are in Pending or Scheduled state
query = {
    "query": {
        "flow_run": {
            "where": {
                "state": {
                    "_in": [EnumValue("Pending"), EnumValue("Scheduled")]
                }
            },
            "select": {
                "id": True,
                "name": True,
                "state": True
            }
        }
    }
}

# Execute the query
response = client.graphql(query)
pending_flows = response.data.flow_run

# Print or process the pending flow runs
for flow in pending_flows:
    print(f"Flow run ID: {flow['id']}, Name: {flow['name']}, State: {flow['state']}")

print("Fetched all flow runs in Pending or Scheduled state.")

### Explanation: 1. Client Initialization: Initialize the Prefect client, which allows you to interact with the Prefect API. 2. GraphQL Query: Define a GraphQL query to fetch all flow runs that are in the

Pending

or

Scheduled

state. 3. Execute Query: Execute the query using the client's

graphql

method. 4. Process Results: Loop through the results and print or process the IDs, names, and states of the pending flow runs. Links to documentation for further reference: - Prefect Client API - Using Prefect Daemonized Services This script effectively checks for jobs that are currently being set up, providing their IDs, names, and current states. As a note, make sure that the client is properly authenticated with your Prefect Cloud or Prefect Server instance to access the required data. Presumably, there was some sense of practicality in providing constructors for these tedious tasks. After all, the quirks and states of modern technologies do tend to boggle the mind. Still, it should run smoothly and get you the information you need.

@Marvin is it possible to suspend a job for a period of time

Yes, it is possible to suspend a job (or flow run) for a specified period of time in Prefect. This can be done using the

suspend_flow_run

function in combination with a timeout parameter. ### Example Usage Here's an example of how you might suspend a flow run for a specific period and resume it afterward:

python
from prefect import task, flow, suspend_flow_run, resume_flow_run
import time

@task
def example_task():
    print("Task is running...")

@flow
def example_flow():
    example_task()
    print("Suspending the flow run for 60 seconds...")
    # Suspend the flow with a timeout of 60 seconds
    suspend_flow_run(timeout=60)
    print("Flow run resumed.")
    example_task()

if __name__ == "__main__":
    example_flow()

### Explanation: 1. Import Required Functions: Import the necessary functions from Prefect. 2. Define Tasks: Define the tasks you want to run within your flow. 3. Suspend Flow: Use the

suspend_flow_run

function within your flow. Here, we specify a timeout of 60 seconds. This means the flow run will be suspended for 60 seconds and then automatically fail if not resumed manually before that. 4. Resume Flow: After the time specified, the flow will be ready to be resumed by calling

resume_flow_run

. ### Important Notes: - Default Timeout: The default timeout for suspended flow runs is one hour. You can specify a different timeout by using the

timeout

parameter. - Infrastructure Tear Down: When a flow run is suspended, the infrastructure running it is torn down. The flow code will execute again from the beginning when resumed, so use tasks and task caching to avoid recomputing expensive operations. - Manual Resumption: You can manually resume the flow run using the Prefect UI or by calling the

resume_flow_run

function with the

flow_run_id

. ### Documentation Links: For more details, you can refer to the following resources: - Pausing or Suspending a Flow Run - Using `suspend_flow_run` and `resume_flow_run` In summary, suspending a flow run can help manage long-running infrastructure costs and efficiently handle flows that may require delays or manual interactions. Always plan the use of this feature carefully to avoid inadvertent stops or failures in your workflows.